Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery.

ABSTRACT: Spleen tyrosine kinase (Syk) is a crucial mediator of inflammatory processes and a promising therapeutic target for the management of autoimmune disorders, such as immune thrombocytopenia. While several Syk inhibitors are known to date, their efficacy and safety profiles remain suboptimal, necessitating the exploration of novel compounds. The study introduces a novel deep reinforcement learning strategy for drug discovery, specifically designed to identify new Syk inhibitors. The approach integrates quantitative structure-activity relationship (QSAR) predictions with generative modelling, employing a stacking-ensemble model that achieves a correlation coefficient of 0.78. From over 78,000 molecules generated by this methodology, we identified 139 promising candidates with high predicted po

SUBMITTER: Zavadskaya M 

PROVIDER: S-EPMC11998205 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature